Which techniques can achieve neural doodle in real-time?
Most of the algorithms (based on image synthesis and style transfer, e.g. neural-doodle) haven't been proven to be highly effective in terms of real-time image processing.

However the following studies discusses such algorithms for real-time texture synthesis:


Feed-forward Synthesis of Textures and Stylized Images

The approach is to move the computational burden to a learning stage, making trained network (CNN) light-weight and compact in order to generate multiple samples of the same texture. This can generate textures as good as comparable to Gatys~et~al, but significantly faster.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution

This method uses parallel work which can generate high-quality images by defining and optimizing loss functions based on high-level features extracted from pretrained networks.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

This uses precomputed feed-forward networks that captures the feature statistics of Markovian patches in order to generate outputs of arbitrary dimensions. This can be applied to texture synthesis, style transfer and video stylization.


Source: Above list suggested on neural-doodle project.